1. Field of the Invention
The present invention relates generally to a method of detecting an object, and more particularly, to a method of detecting an object using a camera, wherein a detection window is created for every area in image data to which the object is input, and a similarity between a histograms is maximized through a comparison of a similarity for each of the pixels on the basis of a calculation of the histograms between the object and the detection window, such that the detection window converges to the location of the object.
2. Description of the Related Art
A method for detecting an object through use of a mobile camera, such as detecting a user's hand, for example, frequently varies in its configuration. Such a method generally includes pre-establishing a recognizable configuration of the hand or a pattern of the hand, and detecting the configuration of the hand within an image input through the camera.
Many methods of detecting an object through use of a mobile camera include defining respective objects corresponding to input signals; and comparing a pre-learned or a pre-stored object with an object in the current input image. Various methods of detecting an object using the camera may be classified into categories such as a global area detection method, color detection method, and a differential image detection method, which are described in further detail as follows:
Global Area Detection Method
The global area detection method has been developed for various applications including face detection, intruder detection and detection of a vehicle's license plate. As illustrated in FIG. 1, the method for detecting a global area is includes sequential detection. More specifically, the global area detection method primarily includes modeling an object through statistical learning or extracting a local characteristic; storing information obtained from the modeling; and detecting the object by sequentially investigating a global area of an input image in a direction from a left-upper side to a right-lower side of the image. The global area detection method requires many calculations due to the moving, comparing and analyzing performed over the global area. Therefore, the global area detection method is not applied to a system having a limited calculation capability. Additionally, it is impossible to detect an object like a hand through a global area detection method, since the configuration of the hand variously changes, and this method is designed without any consideration of rotational changes of an object. The global area detection method often fails, due to a blur phenomenon that occurs when an object such as a hand moves near the camera quickly.
Color Detection Method
As illustrated in FIG. 3, the color detection method enables an object to be detected by defining a range within a color space for the color of the object and obtaining pixels that fall within the range. This method has the disadvantage in that it is difficult to use the color detection method for modeling in circumstances where the object to be detected does not have a single color, such as people's skin color, for example.
Since all objects having colors within the defined color range may be detected as an object, unwanted objects in other locations may be obtained, thereby making it more difficult to determine the correct location of the desired object.
Differential Image Detection Method
As illustrated in FIG. 2, according to the differential image method, and object is detected by obtaining a difference between two consecutive frames and sensing a change in a pixel value. In the differential image method, it is impossible to detect the object when the camera shakes or moves. Even if the object is detected using this method, the boundary in the edge information may be unclear after grouping pixels located in the object. In a variation of the differential image detection method, the object is detected by learning and storing a background model and renewing the background model according to change in time. The variation of the differential image detection method requires calculation of a mean value and a dispersion value in each channel, for each pixel in each consecutive frame. Accordingly, the differential image method is inappropriate for systems that have limited calculation capabilities and limited memory capacities.
Further, the differential image method is unable recognize the location of an object that has limited movement or is completely stationary.